New Heuristic Attribute Reduction Algorithm Based on Rough Set

被引：1
|
作者：
Fang, Weiwei [1 ,2 ]
Yang, Bingru [1 ]
Zhou, Changsheng [2 ]
Hou, Wei [1 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Informat Engn, Beijing 100083, Peoples R China
[2] Beijing Informat Sci & Technol, Beijing 100192, Peoples R China
来源：
2008 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-11 | 2008年
关键词：
Data mining; Rough Set; Attribute Reduction; KDD;
D O I：
10.1109/CCDC.2008.4598070
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
One of the main obstacles facing current data mining techniques is attribute reduction. This paper summarized advantages and disadvantages of current attribute reduction algorithm, and proposed a new attribute reduction method that is taken correlation degree as heuristic information; this method can remove not only irrelevant attributes, but also redundant attributes from the candidate attribute set. Theoretical analysis and experiment results demonstrate that on the premise of unchanged classification precision, the algorithm can obtain the best attribute reduce set effectively and efficiently.
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页码：3936 / +
页数：2
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